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Author:

Wang, Shaohua (Wang, Shaohua.) | Liu, Jianzhen (Liu, Jianzhen.) | Chen, Ning (Chen, Ning.) | Xiao, Jinjian (Xiao, Jinjian.) | Wei, Panyi (Wei, Panyi.)

Indexed by:

SCIE

Abstract:

In this research, 3356 alcohol-related traffic crashes were obtained from blood-alcohol test reports in Tianjin, China. Population density, intersection density, road density, and alcohol outlet densities, including retail density, entertainment density, restaurant density, company density, hotel density, and residential density, were extracted from 2114 traffic analysis zones (TAZs). After a spatial autocorrelation test, the multiple linear regression model (MLR), geographically weighted Poisson regression model (GWPR), and semi-parametric geographically weighted Poisson regression model (SGWPR) were utilized to explore the spatial effects of the aforementioned variables on drunk-driving crash density. The result shows that the SGWPR model based on the adaptive Gaussian function had the smallest AICc value and the best-fitting accuracy. The residential density and the intersection density are global variables, and the others are local variables that have different influences in different regions. Furthermore, we found that the influence of local variables in the economic-technological development area shows significantly different characteristics compared with other districts. Thus, a comprehensive consideration of spatial heterogeneity would be able to improve the effectiveness of the programs formulated to decrease drunk driving crashes.

Keyword:

drunk-driving crashes geographically weighted Poisson regression spatial heterogeneity alcohol availability

Author Community:

  • [ 1 ] [Wang, Shaohua]Tianjin Univ Technol & Educ, Sch Automobile & Transportat, Tianjin 300222, Peoples R China
  • [ 2 ] [Xiao, Jinjian]Tianjin Univ Technol & Educ, Sch Automobile & Transportat, Tianjin 300222, Peoples R China
  • [ 3 ] [Liu, Jianzhen]JIAOKE Transport Consultants Ltd, Beijing 100191, Peoples R China
  • [ 4 ] [Chen, Ning]Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
  • [ 5 ] [Wei, Panyi]Minist Transport, Res Inst Highway, Beijing 100088, Peoples R China

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Source :

APPLIED SCIENCES-BASEL

Year: 2023

Issue: 21

Volume: 13

2 . 7 0 0

JCR@2022

Cited Count:

WoS CC Cited Count: 3

SCOPUS Cited Count: 5

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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